JMH和Disrupter

JMH（Java Microbenchmark Harness）

官方地址：http://openjdk.java.net/projects/code-tools/jmh/

添加依赖，官方地址：https://mvnrepository.com/artifact/org.openjdk.jmh/jmh-core

<!-- https://mvnrepository.com/artifact/org.openjdk.jmh/jmh-core -->
<dependency>
    <groupId>org.openjdk.jmh</groupId>
    <artifactId>jmh-core</artifactId>
    <version>1.21</version>
</dependency>

添加依赖，官方地址：https://mvnrepository.com/artifact/org.openjdk.jmh/jmh-generator-annprocess

<!-- https://mvnrepository.com/artifact/org.openjdk.jmh/jmh-generator-annprocess -->
<dependency>
    <groupId>org.openjdk.jmh</groupId>
    <artifactId>jmh-generator-annprocess</artifactId>
    <version>1.21</version>
    <scope>test</scope>
</dependency>

IDEA安装JMH插件和配置

允许JMH对注解进行处理

添加注解测试

public class Test{
  @Benchmark
  @Warmup(iterations = 1, time = 3)
  @Fork(5)
  @BenchmarkMode(Mode.Throughput)
  @Measurement(Iterations = 1, time = 3)
  public void testCode(){
    // coding
  }
}
  • benchmark

指定测试哪段代码

  • warmup

预热，由于JVM中对于特定代码会存在优化（本地化），预热对于测试结果很重要

  • fork

指定线程数

  • benchmarkmode

基准测试的模式

  • measurement

总共执行多少次

Disruptor

官方地址：http://lmax-exchange.github.io/disruptor/

github地址：https://github.com/LMAX-Exchange/disruptor

特点

对比ConcurrentLinkedQueue

  • 链表实现

JDK中没有ConcurrentArrayQueue

Disruptor是数组实现的

  • 无锁，高并发，使用环形Buffer，直接覆盖（不用清除）旧的数据，降低GC频率
  • 实现了基于事件的生产者消费者模式（观察者模式）

RingBuffer

  • 环形队列
  • RingBuffer的序号，指向下一个可用的元素

采用数组实现，没有首尾指针

对比ConcurrentLinkedQueue，用数组实现的速度更快

假如长度为8，当添加到第12个元素的时候在哪个序号上呢？用12%8决定

当Buffer被填满的时候到底是覆盖还是等待，由Producer决定

长度设为2的n次幂，利于二进制计算，例如：12%8 = 12 & (8 - 1)  pos = num & (size -1)

Disruptor开发步骤

定义Event - 队列中需要处理的元素

定义Event工厂，用于填充队列

  • 效率：disruptor初始化的时候，会调用Event工厂，对ringBuffer进行内存的提前分配
  • GC频率会降低

定义EventHandler（消费者），处理容器中的元素

事件发布模板

long sequence = ringBuffer.next();  // Grab the next sequence
try {
    LongEvent event = ringBuffer.get(sequence); // Get the entry in the Disruptor
    // for the sequence
    event.set(8888L);  // Fill with data
} finally {
    ringBuffer.publish(sequence);
}

使用EventTranslator发布事件

EventTranslator<LongEvent> translator1 = new EventTranslator<LongEvent>() {
  @Override
  public void translateTo(LongEvent event, long sequence) {
  event.set(8888L);
  }
};

ringBuffer.publishEvent(translator1);

//===============================================================
EventTranslatorOneArg<LongEvent, Long> translator2 = new EventTranslatorOneArg<LongEvent, Long>() {
    @Override
    public void translateTo(LongEvent event, long sequence, Long l) {
        event.set(l);
    }
};

ringBuffer.publishEvent(translator2, 7777L);

//===============================================================
EventTranslatorTwoArg<LongEvent, Long, Long> translator3 = new EventTranslatorTwoArg<LongEvent, Long, Long>() {
    @Override
    public void translateTo(LongEvent event, long sequence, Long l1, Long l2) {
        event.set(l1 + l2);
    }
};

ringBuffer.publishEvent(translator3, 10000L, 10000L);

//===============================================================
EventTranslatorThreeArg<LongEvent, Long, Long, Long> translator4 = new EventTranslatorThreeArg<LongEvent, Long, Long, Long>() {
    @Override
    public void translateTo(LongEvent event, long sequence, Long l1, Long l2, Long l3) {
        event.set(l1 + l2 + l3);
    }
};

ringBuffer.publishEvent(translator4, 10000L, 10000L, 1000L);

//===============================================================
EventTranslatorVararg<LongEvent> translator5 = new EventTranslatorVararg<LongEvent>() {

    @Override
    public void translateTo(LongEvent event, long sequence, Object... objects) {
        long result = 0;
        for(Object o : objects) {
            long l = (Long)o;
            result += l;
        }
        event.set(result);
    }
};

ringBuffer.publishEvent(translator5, 10000L, 10000L, 10000L, 10000L);

使用Lamda表达式

package com.test.disruptor;

import com.lmax.disruptor.RingBuffer;
import com.lmax.disruptor.dsl.Disruptor;
import com.lmax.disruptor.util.DaemonThreadFactory;

public class Main03
{
    public static void main(String[] args) throws Exception
    {
        // Specify the size of the ring buffer, must be power of 2.
        int bufferSize = 1024;

        // Construct the Disruptor
        Disruptor<LongEvent> disruptor = new Disruptor<>(LongEvent::new, bufferSize, DaemonThreadFactory.INSTANCE);

        // Connect the handler
        disruptor.handleEventsWith((event, sequence, endOfBatch) -> System.out.println("Event: " + event));

        // Start the Disruptor, starts all threads running
        disruptor.start();

        // Get the ring buffer from the Disruptor to be used for publishing.
        RingBuffer<LongEvent> ringBuffer = disruptor.getRingBuffer();


        ringBuffer.publishEvent((event, sequence) -> event.set(10000L));

        System.in.read();
    }
}

ProducerType生产者线程模式

ProducerType有两种模式

  • Producer.MULTI，默认，表示在多线程模式下产生sequence
  • Producer.SINGLE

如果确认是单线程生产者，那么可以指定SINGLE，效率会提升

如果是多个生产者（多线程），但模式指定为SINGLE，会出问题

等待策略

  • BlockingWaitStrategy：通过线程阻塞的方式，等待生产者唤醒，被唤醒后，再循环检查依赖的sequence是否已经消费
  • BusySpinWaitStrategy：线程一直自旋等待，可能比较耗cpu
  • LiteBlockingWaitStrategy：线程阻塞等待生产者唤醒，与BlockingWaitStrategy相比，区别在signalNeeded.getAndSet
  • 如果两个线程同时访问，一个访问waitfor,一个访问signalAll时，可以减少lock加锁次数
  • LiteTimeoutBlockingWaitStrategy：与LiteBlockingWaitStrategy相比，设置了阻塞时间，超过时间后抛异常
  • PhasedBackoffWaitStrategy：根据时间参数和传入的等待策略来决定使用哪种等待策略
  • TimeoutBlockingWaitStrategy：相对于BlockingWaitStrategy来说，设置了等待时间，超过后抛异常
  • YieldingWaitStrategy：尝试100次，然后Thread.yield()让出cpu
  • SleepingWaitStrategy：sleep

消费者异常处理

  • 默认，disruptor.setDefaultExceptionHandler()
  • 覆盖，disruptor.handleExceptionFor().with()
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原文地址：https://www.cnblogs.com/YC-L/p/14441185.html